No abstract

No abstract

This paper presents a proposed technique for security constrained optimal dispatch (SCOD) problem under normal and emergency conditions. In this technique a modified version of the genetic algorithm (GA) is used. The SCOD problem is formulated using non-linear unit cost functions and solved by the proposed technique. The results obtained using this technique are compared with those obtained using a conventional linear programming technique and with those obtained using fuzzy modeling technique. The comparison studies are performed considering the changes in system constraints as: membership models in f i m y technique and chromosomes in GA technique. Numerical studies of fuzzy modeling are based on the fuzzy linear programming (FLP) technique with fuzzy constraints of different shapes for their membership functions. Simulation results show that the proposed GAbased technique for SCOD is more accurate and efficient, especially with increasing the system size.

No abstract

In this paper, superconducting generator (SCG) stability enhancement via coordinated design of a governor controller (GC) and a static VAR compensator (SVC)-based fuzzy logic stabilizer is investigated. The GC is a conventional lead stabilizer activated by the speed error signal, while the signal produced by the SVC-based stabilizer is based on the SCG speed deviation and acceleration, and on two fuzzy membership functions reflecting few simple control rules. An objective function is defined and the design problem of efficient GC and SVC-based fuzzy stabilizer is formulated as an optimization problem. Particle swarm optimization (PSO) technique is employed to search for optimal parameters of GC and SVC-based stabilizer. Simulation results show that the proposed PSO-tuned control scheme provides good damping to the SCG, and enhances its stability over a range of operating conditions.